Vehicles and methods of controlling a vehicle to accommodate vehicle cut-in

Abstract

Vehicles and methods of predicting a surrounding vehicle cut-in and controlling the vehicle to accommodate the vehicle cut-in are disclosed. In one embodiment, a vehicle includes one or more sensors operable to generate data of a surrounding vehicle, one or more processors, and a non-transitory computer-readable medium storing computer-executable instructions. When the computer-executable instructions are executed by the one or more processors, the one or more processors receive the data of the surrounding vehicle, and extract one or more features from the data of the surrounding vehicle. Based on the one or more features, the one or more processors are controlled to determine a probability that the surrounding vehicle will cut in front of the vehicle, and to control the vehicle in accordance with the probability that the surrounding vehicle will cut in front of the vehicle.

The invention claimed is: 
     
       1. A vehicle comprising:
 one or more sensors operable to generate data of a surrounding vehicle; 
 one or more processors; and 
 a non-transitory computer-readable medium storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to:
 receive the data of the surrounding vehicle; 
 extract one or more features from the data of the surrounding vehicle; 
 generate a vehicle classification profile of the surrounding vehicle; 
 based on the one or more features of the vehicle classification profile, determine a probability that the surrounding vehicle will cut in front of the vehicle; and 
 control the vehicle in accordance with the probability that the surrounding vehicle will cut in front of the vehicle, 
 wherein the probability that the surrounding vehicle will cut in front of the vehicle is determined by comparing the vehicle classification profile having the one or more features of the surrounding vehicle to historical data of previously observed surrounding vehicles. 
 
 
     
     
       2. The vehicle of  claim 1 , wherein the one or more features are provided in one or more distributions, and the probability that the surrounding vehicle will cut in front of the vehicle is determined at least in part by comparing the one or more distributions with one or more historical distributions. 
     
     
       3. The vehicle of  claim 1 , wherein the data of the surrounding vehicle comprises a speed profile including speed of the surrounding vehicle over a period of time. 
     
     
       4. The vehicle of  claim 3 , wherein the one or more features are extracted by performing a Fourier transform on the speed profile. 
     
     
       5. The vehicle of  claim 1 , wherein the data of the surrounding vehicle comprises a lateral movement profile including lateral movement of the surrounding vehicle over a period of time. 
     
     
       6. The vehicle of  claim 5 , wherein the one or more features are extracted by performing a Fourier transform on the lateral movement profile. 
     
     
       7. The vehicle of  claim 1 , wherein:
 the one or more sensors are further operable to generate vehicle data of the vehicle; 
 the computer-executable instructions further cause the one or more processors to:
 receive the vehicle data; and 
 extract one or more vehicle features from the vehicle data; 
 
 the probability that the surrounding vehicle will cut in front the vehicle is based at least in part on the one or more vehicle features. 
 
     
     
       8. The vehicle of  claim 7 , wherein the probability that the surrounding vehicle will cut in front the vehicle is determined by:
 comparing the one or more features of the surrounding vehicle to historical data of prior surrounding vehicles; and 
 comparing the one or more vehicle features of the vehicle to historical data of prior operation of the vehicle. 
 
     
     
       9. The vehicle of  claim 8 , wherein:
 the one or more features are provided in one or more distributions, and the probability that the surrounding vehicle will cut in front of the vehicle is determined at least in part by comparing the one or more distributions with one or more historical distributions; and 
 the one or more vehicle features of the vehicle are provided in one or more vehicle distributions and the probability that the surrounding vehicle will cut in front of the vehicle is determined at least in part by comparing the one or more distributions with one or more historical vehicle distributions. 
 
     
     
       10. A method of controlling a vehicle, the method comprising:
 generating, by one or more sensors, data of a surrounding vehicle; 
 extracting one or more features from the data of the surrounding vehicle; 
 generating a vehicle classification profile of the surrounding vehicle; 
 based on the one or more features of the vehicle classification profile, determining a probability that the surrounding vehicle will cut in front of the vehicle; and 
 controlling the vehicle in accordance with the probability that the surrounding vehicle will cut in front of the vehicle, 
 wherein the probability that the surrounding vehicle will cut in front of the vehicle is determined by comparing the one or more features of the surrounding vehicle of the vehicle classification profile to historical data of previously observed surrounding vehicles. 
 
     
     
       11. The method of  claim 10 , wherein the one or more features are provided in one or more distributions, and the probability that the surrounding vehicle will cut in front of the vehicle is determined at least in part by comparing the one or more distributions with one or more historical distributions. 
     
     
       12. The method of  claim 10 , wherein the data of the surrounding vehicle comprises a speed profile including speed of the surrounding vehicle over a period of time. 
     
     
       13. The method of  claim 12 , wherein the one or more features are extracted by performing a Fourier transform on the speed profile. 
     
     
       14. The method of  claim 10 , wherein the data of the surrounding vehicle comprises a lateral movement profile including lateral movement of the surrounding vehicle over a period of time. 
     
     
       15. The method of  claim 14 , wherein the one or more features are extracted by performing a Fourier transform on the lateral movement profile. 
     
     
       16. The method of  claim 10 , further comprising
 generating vehicle data of the vehicle; and 
 extracting one or more vehicle features from the vehicle data, wherein the probability that the surrounding vehicle will cut in front of the vehicle is based at least in part on the one or more vehicle features. 
 
     
     
       17. The method of  claim 16 , the probability that the surrounding vehicle will cut in front of the vehicle is determined by:
 comparing the one or more features of the surrounding vehicle to historical data of prior surrounding vehicles; and 
 comparing the one or more vehicle features of the vehicle to historical data of prior operation of the vehicle. 
 
     
     
       18. The method of  claim 17 , wherein:
 the one or more features are provided in one or more distributions, and the probability that the surrounding vehicle will cut in front of the vehicle is determined at least in part by comparing the one or more distributions with one or more historical distributions; and 
 the one or more vehicle features of the vehicle are provided in one or more vehicle distributions and the probability that the surrounding vehicle will cut in front of the vehicle is determined at least in part by comparing the one or more distributions with one or more historical vehicle distributions. 
 
     
     
       19. The vehicle of  claim 1 , wherein the one or more vehicle features of the vehicle classification profile include a vehicle type, a vehicle make, a vehicle year, a vehicle color, or a vehicle condition. 
     
     
       20. The method of  claim 10 , wherein the one or more vehicle features of the vehicle classification profile include a vehicle type, a vehicle make, a vehicle year, a vehicle color, or a vehicle condition.